Gaining timely insights on real-world emergency events, such as infectious disease outbreaks, is critical for developing appropriate response strategies. In this work, we propose a data-driven approach to study the spreading dynamics of the global Covid-19 pandemic. Specifically, we aim to identify a set of most “similar” geographic regions as proxies for making predictions on a targeted location. Example predictions include the number of new cases, number of hospitalizations, and number of deaths. Such predictions can be made at different levels of regional granularities, including city, county, and state levels. Our approach starts by transforming regional time series into graph representations using the natural visibility graph (NVG) model in order to capture their intrinsic trends and properties. These graphs are then projected onto a common embedding space using graph-level network embedding techniques. Essentially, each time series is converted as a data point in a feature embedding space, where spatial proximity indicates similarity among time series. Given a targeted region, our approach can identify the most “relevant” geographic regions by finding its k-nearest neighbors in the embedding space. Subsequently, appropriate response strategies and policies (e.g., school shutdown, indoor dining restriction) can be adapted based on the success or failure experiences from relevant regions. Our approach will potentially provide valuable insights in mitigating the spreading of infectious disease.

Subgraph Matching is one of the fundamental problems in network analysis, with a wide range of applications ranging from drug repurposing and discovery to programming language analysis. Due to the increasing prevalence of knowledge graphs (KGs), there has been growing interests in extending existing subgraph matching algorithms to the KG domain. One of the main challenges here lies on the structural gap, which refers to the difference between the query pattern and the corresponding subgraph instance due to variations in semantic expression. To address this challenge, we propose a semantic guided subgraph matching method for knowledge graphs, which extends our prior filtering-based method. Specifically, our approach leverages an external semantic ontology to estimate the overall fitness/quality of subgraph candidates with respect to the query template. In addition, our approach incorporates effective query decomposition strategies to reduce the overall query cost. Furthermore, we develop a distributed implementation of the algorithm such that it can be scaled up to handle knowledge graphs with a large number of entities and relations. We demonstrate the effectiveness of the proposed approach on a variety of semantic networks provided in the DARPA Modeling Adversarial Activity (MAA) program.

We present the design and implementation of an automated event summarization system that leverages publicly available data from online sources. A novel Network of Networks (NoN) model is proposed to represent a multimodal data set comprising microblog posts, news articles, and images that describe current attitudes, trends, and events being shared by individuals and organizations. In this model, networks are arranged in layers that represent the different modalities, and the nodes within a given layer account for the information in that modality. Edges connect pairs of nodes - possibly from different modalities - based on the topical similarity of the content. A novel ranking algorithm is developed that selects a topically diverse collection of nodes across the different layers (i.e., tuples) that serve as representative highlights of the events described in the network. Tuples are subsequently stitched together in a temporal sequence in order to generate a consistent event storyline. This stitching is accomplished by framing the problem as a modified longest path problem in a directed acyclic graph. Our proposed system has been fully implemented using parallel computing paradigms for scalable data processing and real-time analysis, and initial experiments have been conducted on real-world events to demonstrate the effectiveness of the system.

The tides of sentiments expressed in online social media rise and fall. In recent years, the availability of big data has afforded researchers the ability to develop and evaluate techniques that allow us to identify, classify, aggregate, and even predict the sentiment dynamics for nearly any topic [1], [2]. The users of online social media platforms like Twitter are able to create, propagate, and consume information pertaining to any conceivable topic, and in doing so, they influence each other's opinions and behavior. Herding behavior and online sentiment are mutually reinforcing, and have been shown to influence consumer purchasing decisions [3], [4].